LISynSeg: Data-Centric Label-to-Image Synthesis for Cross-Modality Whole-Heart Segmentation
Paper recorded by Signals 4 on 2026-08-31 in cs.CV. Abstract reproduced from arXiv; link to the original below.
Published 2026-08-31 on arXiv · recorded by Signals 4 on 2026-09-01
Category: cs.CV · 计算机视觉 · first seen 2026-09-01
Abstract
Whole-heart segmentation (WHS) in computed tomography (CT) and magnetic resonance imaging (MRI) is affected by acquisition shifts and heterogeneous cardiac annotations. Existing WHS systems combine architectural design, transfer learning, and generic spatial or intensity augmentation. We investigate whether changes to data augmentation and training supervision can improve cross-modality WHS while
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Cite this page: LISynSeg: Data-Centric Label-to-Image Synthesis for Cross-Modality Whole-Heart Segmentation: the #198 most recent of 237 cs.CV papers we have recorded (as of 2026-08-31). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/lisynseg-data-centric-label-to-image-synthesis-for-cross-modality-whole-heart-se.html
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